Data Skor Proyek Final Berdasarkan Metode Pengajaran - Kasus 12

# --- Data Skor Proyek Final Berdasarkan Metode Pengajaran ---
skor <- c(75, 80, 72, 78, 70,         # Metode A
          82, 88, 85, 90, 80, 86,     # Metode B
          88, 92, 95, 85, 90, 87, 93, # Metode C
          78, 82, 80, 75, 85, 77)     # Metode D

metode <- factor(c(rep("A", 5),
                   rep("B", 6),
                   rep("C", 7),
                   rep("D", 6)))

# --- Buat Data Frame Gabungan ---
data_adk <- data.frame(Metode = metode, Skor = skor)

# --- Lakukan Uji ANOVA ---
hasil_anova <- aov(Skor ~ Metode, data = data_adk)

# --- Tampilkan Ringkasan Hasil Uji ANOVA ---
cat("--- Hasil Uji ANOVA ---\n")

## --- Hasil Uji ANOVA ---

summary(hasil_anova)

##             Df Sum Sq Mean Sq F value  Pr(>F)    
## Metode       3  764.6  254.88   18.31 5.9e-06 ***
## Residuals   20  278.3   13.92                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# --- OPTIONAL: Uji Lanjutan Jika Hasil Signifikan (Post-hoc Test) ---
cat("\n--- Uji Lanjutan (Tukey HSD) Jika Diperlukan ---\n")

## 
## --- Uji Lanjutan (Tukey HSD) Jika Diperlukan ---

hasil_tukey <- TukeyHSD(hasil_anova)
print(hasil_tukey)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Skor ~ Metode, data = data_adk)
## 
## $Metode
##           diff         lwr        upr     p adj
## B-A  10.166667   3.8440578 16.4892756 0.0011546
## C-A  15.000000   8.8861159 21.1138841 0.0000064
## D-A   4.500000  -1.8226089 10.8226089 0.2240393
## C-B   4.833333  -0.9757504 10.6424170 0.1248350
## D-B  -5.666667 -11.6950377  0.3617044 0.0700958
## D-C -10.500000 -16.3090837 -4.6909163 0.0003242

# --- OPTIONAL: Visualisasi Boxplot Perbandingan Skor ---
boxplot(Skor ~ Metode,
        data = data_adk,
        main = "Perbandingan Skor Proyek Final per Metode",
        xlab = "Metode Pengajaran",
        ylab = "Skor Proyek Final",
        col = c("skyblue", "lightgreen", "lightcoral", "gold"))